Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.

ABSTRACT: Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual

SUBMITTER: Fuadah YN 

PROVIDER: S-EPMC10584148 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature